Evaluation of breast cancer PDX tumor heterogeneity at single cell resolution II
Single-cell RNA-seq · human · 23 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Topic
- Breast cancerThis series is part of our curated briefing on it.
- Samples
- 23 samples: 23 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Single-cell transcriptional atlas of human breast cancers and model systemsClinical and translational medicine · 2024
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE276609_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1158224 and SRA study SRP531277. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 23 sample titles: 23 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8502984 BCM-2147_109176, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502985 BCM-2147CR_109178, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502986 BCM-7482CR_109078, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502987 HCI-001_105983, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502988 HCI-013_109115, HCI-013_109115, ER+ breast cancer patient-derived xenograft
- GSM8502989 HCC1143, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502990 HCC1187, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502991 MDA468, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502992 SUM149, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502993 UCD52, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502994 VCU-BC-001, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502995 VCU-BC-002, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502996 VCU-BC-003_CMMC1410, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502997 VCU-BC-004_CMMC1414, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502998 VCU-BC-006_CMMC1416, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8502999 VCU-BC-024_CMMC1423, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503000 WHIM2_106305, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503001 WHIM2_106361, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503002 WHIM30_105954, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503003 WHIM30_108896, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503004 WHIM30-O_108793-4, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503005 WHIM30CR_108903, Basal-like triple-negative breast cancer patient-derived xenograft
- GSM8503006 WHIM30CR-O_108805-9, Basal-like triple-negative breast cancer patient-derived xenograft
- GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing · shares breast
- GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing · shares breast
- GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing · shares breast
- GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing · shares breast
- GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
- GSE243375 HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial 287 samples shares heterogeneity, breast
- GSE298567 Single-Cell transcriptomics reveals biomarker heterogeneity linked to CDK4/6 Inhibitor resistance in breast cancer cell lines 14 samples shares heterogeneity, breast
- GSE269415 Bioprinted spatially defined breast tumor microenvironment models of intratumoral heterogeneity and drug resistance 18 samples shares heterogeneity, breast
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.